In order to incorporate feedback into the RL algorithm, we build upon the Advise algorithm [1]} and thus provide a brief description of the approach.
Advise assumes binary feedback from a trainer that returns either `right' or `wrong' for a particular agent's choice of action.
The feedback is accumulated for each state-action pair separately, and it is used to derive a trainer's policy, denoted by \(\pi _{F}\left(s,a\right)\) , which is then used to modify the agent's policy.
Additionally, \(C\)  is defined as the probability that the trainer gives the right (consistent) feedback, and assuming a binomial distribution, the trainer's policy is as follows.
\( \pi _{F}\left(s,a\right) = C^{\Delta (s,a)} (1-C)^{\sum _{j\ne a}\Delta (s,j)}\) 
